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software-estimation

Prediction of software development cost is an extremely important task before starting the actual development phase. Software products are acceptable by clients as long as they are developed within the lower budget. Software estimation is one of the most challenging areas of project management. Machine learning algorithms are used to handle these type of problems. Machine learning algorithms increase project success rates. software simulation using machine learning algorithms could further enhance project estimation methods and contribute to better resource allocation and utilization. The proposed effort and duration estimation models are intended to serve as a decision support tool for any organization developing and implementing software systems. ISBSG dataset is used for this implementation. Results show that machine learning models can be used to predict software cost with high accuracy rate. Keywords : ISBSG,Software project estimation,Effort and duration estimation, Prediction.

spark-jupyter-aws

A guide on how to set up Jupyter with Pyspark painlessly on AWS EC2 clusters, with S3 I/O support

speech-recognition

NLP speech audio

speech-recognition-nlp

In this project we will build a model for speech to text using Python

stock-prediction

In following case of study, we will process the data of purchase and sales of a three year period of the company for analysis and modeling to obtain a prediction of stock quantity to be order.

store-sale-demand-forecast

Demand Forecasting is the process in which historical sales data is used to develop an estimate of an expected forecast of customer demand. I worked on the Store Item Demand Forecasting dataset available at Kaggle (https://www.kaggle.com/c/demand-forecasting-kernels-only) . The dataset consists of 10 stores and 50 items and their respective sales . In my project i used the plotly and seaborn visualization libraries for plotting which are an excellent tool to get insights into the data. Feature engineering was performed to get the right features for predicting the sales.I used the following ML models : Gradient Boosting Regressor ,Decision Tree Regressor ,Linear SVR ,Random forest Regressor and compared the performance . Finally, deep learning implementation is also done using LSTM.

supply-chain-analytics-project

Demand forecasting of items using three step machine learning model. Clustering - Classification - Prediction

svhn-cnn

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

svhn-multi-digit

svhnproject

A machine learning project using various ML techniques to try to correctly classify single digits that appear on Google's Street View images of house addresses.

time-series-sales-forecasting-for-agricultural-equipments

Monthly Sales forecasting for agricutural Equipment using univariate and mutlivariate methods

transfer-learning-tiny-examples

Here are two examples transfer learning examples based on imageNet pre_trained Networks.

transfer-learning-using-imagenet

In this notebook, we'll Walk through how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on ImageNet available from torchvision.

youtube-comments-spam-classifier

In order to understand NLP in practice, we will work through a simple example of a spam classifier using sklearn.